A Preliminary Study on Symbolic Fuzzy Cognitive Maps for Pattern Classification

Mabel Frias*, Gonzalo Nápoles, Yaima Filiberto, Rafael Bello, Koen Vanhoof

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Within the neural computing field, Fuzzy Cognitive Maps (FCMs) are attractive simulation tools to model dynamic systems by means of well-defined neural concepts and causal relationships, thus equipping the network with interpretability features. However, such components are normally described by quantitative terms, which may be difficult to handle by experts. Recently, we proposed a symbolic FCM scheme (termed FCM-TFN) in which both weights and activation values are described by triangular fuzzy numbers. In spite of the promising results, the model's performance in solving prediction problems remains uncertain. In this paper, we explore the prediction capabilities of the FCM-TFN model in pattern classification and concluded that our method is able to perform well when compared with traditional classifiers.
Original languageEnglish
Title of host publicationApplied Computer Sciences in Engineering
EditorsJuan Carlos Figueroa-García, Mario Duarte-González, Sebastián Jaramillo-Isaza, Alvaro David Orjuela-Cañon, Yesid Díaz-Gutierrez
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages11
ISBN (Print)9783030310196
Publication statusPublished - 2019
Externally publishedYes
Event6th Workshop on Engineering Applications, WEA 2019 -
Duration: 16 Oct 2019 → …


Conference6th Workshop on Engineering Applications, WEA 2019
Abbreviated titleWEA
Period16/10/19 → …


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